Exploring AI Agent Examples & Use Cases That Transform Customer Experience

Key Takeaways

• AI agents for call centers connect systems and automate support workflows, reducing manual handoffs between tools
• Agentic AI use cases include understanding customer intent and completing multi-step actions like updating accounts, processing refunds, or retrieving CRM data
• Voice AI agents and chat AI systems handle routine customer requests such as order tracking, billing questions, and appointment scheduling
• Examples of AI agents also include real-time assistance for human agents, surfacing knowledge and guidance during conversations
• AI agents analyze interactions and automate post-call work, including quality scoring, customer sentiment analysis, and CRM documentation

Introduction

The most practical AI agents in call centers were built to handle one task at a time. A bot answers FAQs. A separate tool logs tickets. A QA team manually reviews a fraction of calls. No single tool is the failure point. The failure is that none of them connect, so agents burn time on hand-offs and managers make decisions from an incomplete picture.

An IBM study of over 3,500 senior executives found that 92% of leaders expect agentic AI to deliver measurable ROI. Contact centers are one of the clearest places to see this impact.

AI agents can interpret customer intent, connect multiple decisions, and take action automatically. For example, they can process refunds, update account details, and pull records from connected systems without waiting for human sign off at every step.

However, these systems are designed to work alongside customer support reps, not replace them. Every deployment should also include a clear escalation path to a human agent for judgment calls, sensitive conversations, or situations that fall outside the defined scope.

This guide covers practical call center use cases, explains how agentic AI differs from traditional AI, explores how AI agents are deployed across industries, and includes real world examples.

The highest-impact deployments tend to share a few traits:

• High call volume with a large share of repeatable request types
• Clear policies that define when an agent can act and when it must escalate
• Connected systems, CRM, order management, and knowledge bases, that the agent can read from and write to
• QA processes that cover more than the 1-2% of calls a human team can realistically review

What is the difference between agentic AI and other AI?

Most AI tools in contact centers fall into two categories: rule-based chatbots that follow decision trees, and generative AI tools or virtual agents that produce responses but leave a human to decide what to do with them. Both stop short of taking action, which means a rep still has to read the output, switch to another system, and complete the request manually.

Agentic AI closes that gap by combining intent detection, multi step planning, and direct execution within connected tools. A single customer request, say a billing dispute tied to a recent order, can require pulling data from a CRM, checking a payment system, and updating a ticket, and an agentic AI handles that whole chain without waiting for human sign-off at each step.

What are the use cases of AI agents for large enterprises?

AI agents are being deployed at specific points in the contact center workflow where volume is high, tasks are repeatable, and speed matters.

1. Voice and chat AI agents handling inbound requests

AI agents handle inbound voice and chat by identifying what a customer needs, pulling relevant data from connected systems CRM, order management, knowledge bases and resolving the request without transferring to a live rep.

Common examples include order status checks, appointment scheduling, returns processing, product questions, account verification, and billing updates.

This reduces queue wait times on high volume, routine interactions, though it requires accurate speech recognition and well-defined handoff protocols for cases the agent cannot resolve.

2. AI agents assisting live reps during conversations

Some AI agents do not interact with customers at all. They work in the background during live conversations, identifying intent and emotional cues, searching internal systems, and surfacing relevant knowledge articles, policy information, and suggested responses directly to the rep.

This improves handle time and first call resolution without the customer knowing AI is involved. The rep stays in control of the conversation while the AI handles the information retrieval that would otherwise mean putting the customer on hold.

3. Automated quality scoring of every interaction

AI agents evaluate every customer interaction against quality standards, identify coaching opportunities, and flag compliance violations as they occur.

Traditional QA processes review only 1-2% of calls. AI scoring closes that gap by covering 100% of interactions, though it requires custom scorecards built around actual business standards rather than generic criteria.

4. Generating customer satisfaction scores without surveys

AI agents infer customer satisfaction by analyzing tone, word choice, and conversation patterns across every interaction without waiting for a customer to complete a post call survey.

This works when models are trained on contact center conversation data rather than general sentiment data, which tends to miss domain specific language and context. During implementation, results should be validated against actual survey responses to confirm that the model is calibrated correctly.

5. Automating after call documentation

After a call ends, AI agents generate summaries, populate CRM fields, create follow-up tasks, and log interaction outcomes by removing the manual data entry that extends handle time and delays reporting.

This requires the model to understand company specific terminology and the structure of the CRM schema it is writing to; otherwise, outputs need significant correction before they are usable.

What are some examples of AI agents?

AI agents are being deployed in industries where customer interactions are high-volume, time-sensitive, and tied to backend systems that a human rep would otherwise have to navigate manually.
The examples below cover those industries, with the specific tasks AI agents handle in each one.

1. AI virtual agents in retail

Retail contact centers deal with two distinct problem types: pre-purchase questions and post-purchase issues. An AI agent can handle both without transferring the customer, pulling product data, order status, and return policy details from connected systems in the same interaction.

• Shopping assistant: Answers questions about features, sizing, pricing, and availability, and surfaces relevant options when a customer describes what they need in plain language
• Post-purchase support: Handles order tracking, returns processing, and basic installation guidance by pulling data from order management and CRM systems directly
• Smart escalation: When a query becomes complex or emotionally charged, the agent transfers to a human rep with the full conversation history attached, so the customer does not repeat themselves

2. AI virtual agents in healthcare

Healthcare organizations use AI agents to improve patient experiences, take administrative workload off front-desk and billing staff. The agents handle the repeatable, rulesdriven tasks that consume time without requiring clinical judgment, and escalate when the situation calls for a human.

• Patient scheduling and intake: Books, reschedules, and cancels appointments by checking live availability and confirming details without staff involvement
• Insurance and billing support: Answers eligibility and coverage questions instantly, reducing the queue for billing representatives on common, repeatable inquiries
• Human escalation: When a situation involves clinical sensitivity or patient distress, the agent transfers to a nurse or care coordinator with full context already attached

3. AI virtual agents in financial services

Financial services firms operate under strict compliance requirements and high interaction volumes, two conditions that make manual oversight impractical at scale. AI agents address both by monitoring transactions and conversations continuously rather than in periodic spot checks.

• Fraud detection and risk management: Monitors transaction patterns and flags anomalies as they occur, triggering a compliance workflow without waiting for a manual review cycle
• 100% interaction monitoring: Scores every conversation against regulatory standards like FDCPA, closing the gap left by human QA teams that can realistically review only 1-2% of calls
• Agent screen monitoring: Captures desktop activity during and after calls alongside conversation analysis, so QA teams see what agents did, not just what they said

4. AI virtual agents in contact centers

Contact centers generate large volumes of repetitive work that sits outside the conversation itself, documentation, scoring, and coaching. AI agents handle that work automatically, which reduces handle time and gives managers better data to act on.

• Post-call automation: Generates call summaries, populates CRM fields, and creates follow-up tasks after every interaction, removing the manual data entry that extends handle time and delays reporting
• Personalized coaching: Identifies performance gaps from real conversations and surfaces coaching recommendations tied to actual behavior, not observations from a small sample of reviewed calls
• Conversation scoring and FDCPA compliance: Scores every conversation against custom quality standards and flags compliance violations as they occur, rather than after a complaint has already been filed

5. AI virtual agents in telecom

Telecom providers handle some of the highest contact volumes of any industry, with a large share of those contacts covering billing disputes, service outages, and plan changes.

Most of those requests follow predictable patterns, which makes them well-suited for AI agents that can pull account data, check network status, and make changes without routing to a live rep.

• Billing and account management: Resolves billing questions, applies credits, and processes plan changes by connecting directly to billing and account management systems, without putting the customer on hold
• Outage detection and updates: Identifies whether a customer's issue is tied to a known network outage, pulls live status data, and gives the customer an accurate update rather than opening an unnecessary technical support ticket
• Device and service troubleshooting: Walks customers through diagnostic steps for connectivity or device issues, and escalates to a technical rep with full context when the issue requires hands-on intervention

Frequently asked questions

1. How do AI agents handle customer interactions differently than traditional chatbots in a contact center?
A. Unlike rule based chatbots in contact centers that follow fixed scripts, AI agents understand customer intent, maintain context, and take action across backend systems without handing off to a human.

2. Will AI replace human call center agents?
A. AI won't fully replace human call center agents but will automate repetitive tasks like FAQs and call routing. Human agents are still needed for complex, emotional, and high-value interactions.Most call centers will adopt a hybrid model where AI supports agents, not replaces them.

3. How does agentic AI boost first-contact resolution rates?
A. By accurately understanding customer intent, accessing data across systems, and taking action in real time, agentic AI minimizes follow-ups and escalations. It can resolve requests like order updates, refunds, or scheduling within a single interaction.

4. What is the ROI of deploying agentic AI in a contact center?
A. Agentic AI delivers ROI by reducing support costs, increasing automation rates, and improving agent productivity. It also boosts customer satisfaction by resolving queries faster and reducing repeat contacts. Calculate potential cost savings and productivity gains from AI adoption with Level AI ROI calculator.

5. How do AI agents ensure compliance when handling sensitive customer information?
A. AI agents are designed with enterprise grade security and governance measures to protect sensitive data and meet industry compliance requirements.

Wrapping up: Choosing the right AI agent platform

AI agents deliver consistent results when three conditions are in place: complete interaction data, strong intent detection built on NLU (not keyword matching), and seamless integration with existing systems. Without these, even advanced tools struggle to operate reliably at scale.

This guide covered the most practical use cases for AI agents in contact centers, from self-service and live agent assistance to automated QA, inferred satisfaction scoring, and post-call automation. It also outlined what to look for when evaluating platforms.

The next step is translating these capabilities into real-world performance. That typically comes down to how well a platform handles end-to-end workflows, not just isolated features.

Platforms like Level AI are built around this approach. In addition to the capabilities discussed above, they offer:

• Agent coaching: Identifies performance gaps in real conversations, prioritizes what to address first, and tracks improvement over time
• Screen recording: Captures agent desktop activity during and after a call alongside conversation analysis, so QA teams see what agents did, not just what they said
• Conversation analytics dashboards: Delivers a unified view of performance data for QA teams, agents, and team leaders, built on 100% of interactions

If you want to evaluate how Level AI performs on your contact center's conversations, click here to schedule a demo.